Quantitative Pharmacokinetic Analysis of Prostate Cancer DCE-MRI at 3T: Comparison of Two Arterial Input Functions on Cancer Detection with Digitized Whole Mount Histopathological Validation.

Citation:

Fennessy FM, Fedorov A, Penzkofer T, Kim KW, Hirsch MS, Vangel MG, Masry P, Flood TA, Chang M-C, Tempany CM, et al. Quantitative Pharmacokinetic Analysis of Prostate Cancer DCE-MRI at 3T: Comparison of Two Arterial Input Functions on Cancer Detection with Digitized Whole Mount Histopathological Validation. Magn Reson Imaging. 2015;33 (7) :886-94. Copy at http://www.tinyurl.com/n28wgm8

Date Published:

2015 Sep

Abstract:

Accurate pharmacokinetic (PK) modeling of dynamic contrast enhanced MRI (DCE-MRI) in prostate cancer (PCa) requires knowledge of the concentration time course of the contrast agent in the feeding vasculature, the so-called arterial input function (AIF). The purpose of this study was to compare AIF choice in differentiating peripheral zone PCa from non-neoplastic prostatic tissue (NNPT), using PK analysis of high temporal resolution prostate DCE-MRI data and whole-mount pathology (WMP) validation. This prospective study was performed in 30 patients who underwent multiparametric endorectal prostate MRI at 3.0T and WMP validation. PCa foci were annotated on WMP slides and MR images using 3D Slicer. Foci ≥0.5cm(3) were contoured as tumor regions of interest (TROIs) on subtraction DCE (early-arterial - pre-contrast) images. PK analyses of TROI and NNPT data were performed using automatic AIF (aAIF) and model AIF (mAIF) methods. A paired t-test compared mean and 90th percentile (p90) PK parameters obtained with the two AIF approaches. Receiver operating characteristic (ROC) analysis determined diagnostic accuracy (DA) of PK parameters. Logistic regression determined correlation between PK parameters and histopathology. Mean TROI and NNPT PK parameters were higher using aAIF vs. mAIF (p<0.05). There was no significant difference in DA between AIF methods: highest for p90 volume transfer constant (K(trans)) (aAIF differences in the area under the ROC curve (Az) = 0.827; mAIF Az=0.93). Tumor cell density correlated with aAIF K(trans) (p=0.03). Our results indicate that DCE-MRI using both AIF methods is excellent in discriminating PCa from NNPT. If quantitative DCE-MRI is to be used as a biomarker in PCa, the same AIF method should be used consistently throughout the study.

Last updated on 01/04/2016